{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "name:曾露莎"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier\n",
    "import xgboost as xgb\n",
    "\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "from sklearn.metrics import log_loss\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lusha/.local/lib/python3.6/site-packages/IPython/core/interactiveshell.py:3018: DtypeWarning: Columns (12,18) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>City</th>\n",
       "      <th>Monthly_Income</th>\n",
       "      <th>DOB</th>\n",
       "      <th>Lead_Creation_Date</th>\n",
       "      <th>Loan_Amount_Applied</th>\n",
       "      <th>Loan_Tenure_Applied</th>\n",
       "      <th>Existing_EMI</th>\n",
       "      <th>Employer_Name</th>\n",
       "      <th>...</th>\n",
       "      <th>Interest_Rate</th>\n",
       "      <th>Processing_Fee</th>\n",
       "      <th>EMI_Loan_Submitted</th>\n",
       "      <th>Filled_Form</th>\n",
       "      <th>Device_Type</th>\n",
       "      <th>Var2</th>\n",
       "      <th>Source</th>\n",
       "      <th>Var4</th>\n",
       "      <th>LoggedIn</th>\n",
       "      <th>Disbursed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID000002C20</td>\n",
       "      <td>Female</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>20000</td>\n",
       "      <td>23-May-78</td>\n",
       "      <td>15-May-15</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>CYBOSOL</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>Web-browser</td>\n",
       "      <td>G</td>\n",
       "      <td>S122</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID000004E40</td>\n",
       "      <td>Male</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>35000</td>\n",
       "      <td>07-Oct-85</td>\n",
       "      <td>04-May-15</td>\n",
       "      <td>200000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TATA CONSULTANCY SERVICES LTD (TCS)</td>\n",
       "      <td>...</td>\n",
       "      <td>13.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6762.9</td>\n",
       "      <td>N</td>\n",
       "      <td>Web-browser</td>\n",
       "      <td>G</td>\n",
       "      <td>S122</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID000007H20</td>\n",
       "      <td>Male</td>\n",
       "      <td>Panchkula</td>\n",
       "      <td>22500</td>\n",
       "      <td>10-Oct-81</td>\n",
       "      <td>19-May-15</td>\n",
       "      <td>600000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ALCHEMIST HOSPITALS LTD</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>Web-browser</td>\n",
       "      <td>B</td>\n",
       "      <td>S143</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID000008I30</td>\n",
       "      <td>Male</td>\n",
       "      <td>Saharsa</td>\n",
       "      <td>35000</td>\n",
       "      <td>30-Nov-87</td>\n",
       "      <td>09-May-15</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>BIHAR GOVERNMENT</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>Web-browser</td>\n",
       "      <td>B</td>\n",
       "      <td>S143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID000009J40</td>\n",
       "      <td>Male</td>\n",
       "      <td>Bengaluru</td>\n",
       "      <td>100000</td>\n",
       "      <td>17-Feb-84</td>\n",
       "      <td>20-May-15</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>GLOBAL EDGE SOFTWARE</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>Web-browser</td>\n",
       "      <td>B</td>\n",
       "      <td>S134</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID  Gender       City  Monthly_Income        DOB  \\\n",
       "0  ID000002C20  Female      Delhi           20000  23-May-78   \n",
       "1  ID000004E40    Male     Mumbai           35000  07-Oct-85   \n",
       "2  ID000007H20    Male  Panchkula           22500  10-Oct-81   \n",
       "3  ID000008I30    Male    Saharsa           35000  30-Nov-87   \n",
       "4  ID000009J40    Male  Bengaluru          100000  17-Feb-84   \n",
       "\n",
       "  Lead_Creation_Date  Loan_Amount_Applied  Loan_Tenure_Applied  Existing_EMI  \\\n",
       "0          15-May-15             300000.0                  5.0           0.0   \n",
       "1          04-May-15             200000.0                  2.0           0.0   \n",
       "2          19-May-15             600000.0                  4.0           0.0   \n",
       "3          09-May-15            1000000.0                  5.0           0.0   \n",
       "4          20-May-15             500000.0                  2.0       25000.0   \n",
       "\n",
       "                         Employer_Name    ...    Interest_Rate Processing_Fee  \\\n",
       "0                              CYBOSOL    ...              NaN            NaN   \n",
       "1  TATA CONSULTANCY SERVICES LTD (TCS)    ...            13.25            NaN   \n",
       "2              ALCHEMIST HOSPITALS LTD    ...              NaN            NaN   \n",
       "3                     BIHAR GOVERNMENT    ...              NaN            NaN   \n",
       "4                 GLOBAL EDGE SOFTWARE    ...              NaN            NaN   \n",
       "\n",
       "  EMI_Loan_Submitted Filled_Form  Device_Type  Var2  Source  Var4 LoggedIn  \\\n",
       "0                NaN           N  Web-browser     G    S122     1        0   \n",
       "1             6762.9           N  Web-browser     G    S122     3        0   \n",
       "2                NaN           N  Web-browser     B    S143     1        0   \n",
       "3                NaN           N  Web-browser     B    S143     3        0   \n",
       "4                NaN           N  Web-browser     B    S134     3        1   \n",
       "\n",
       "  Disbursed  \n",
       "0       0.0  \n",
       "1       0.0  \n",
       "2       0.0  \n",
       "3       0.0  \n",
       "4       0.0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "train = pd.read_csv(\"Train.csv\")\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Monthly_Income</th>\n",
       "      <th>Loan_Amount_Applied</th>\n",
       "      <th>Loan_Tenure_Applied</th>\n",
       "      <th>Existing_EMI</th>\n",
       "      <th>Loan_Amount_Submitted</th>\n",
       "      <th>Loan_Tenure_Submitted</th>\n",
       "      <th>Interest_Rate</th>\n",
       "      <th>Processing_Fee</th>\n",
       "      <th>Var4</th>\n",
       "      <th>LoggedIn</th>\n",
       "      <th>Disbursed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.702000e+04</td>\n",
       "      <td>8.694900e+04</td>\n",
       "      <td>86949.000000</td>\n",
       "      <td>8.694900e+04</td>\n",
       "      <td>5.240700e+04</td>\n",
       "      <td>52407.000000</td>\n",
       "      <td>27726.000000</td>\n",
       "      <td>27420.000000</td>\n",
       "      <td>87020.000000</td>\n",
       "      <td>87020.000000</td>\n",
       "      <td>87019.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.884997e+04</td>\n",
       "      <td>2.302507e+05</td>\n",
       "      <td>2.131399</td>\n",
       "      <td>3.696228e+03</td>\n",
       "      <td>3.950106e+05</td>\n",
       "      <td>3.891369</td>\n",
       "      <td>19.197474</td>\n",
       "      <td>5131.150839</td>\n",
       "      <td>2.949793</td>\n",
       "      <td>0.029350</td>\n",
       "      <td>0.014629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.177511e+06</td>\n",
       "      <td>3.542068e+05</td>\n",
       "      <td>2.014193</td>\n",
       "      <td>3.981021e+04</td>\n",
       "      <td>3.082481e+05</td>\n",
       "      <td>1.165359</td>\n",
       "      <td>5.834213</td>\n",
       "      <td>4725.837644</td>\n",
       "      <td>1.697736</td>\n",
       "      <td>0.168785</td>\n",
       "      <td>0.120063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.000000e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.990000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.650000e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.000000e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>15.250000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.500000e+04</td>\n",
       "      <td>1.000000e+05</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.000000e+05</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000e+04</td>\n",
       "      <td>3.000000e+05</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.500000e+03</td>\n",
       "      <td>5.000000e+05</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>6250.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.445544e+08</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>3.000000e+06</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Monthly_Income  Loan_Amount_Applied  Loan_Tenure_Applied  Existing_EMI  \\\n",
       "count    8.702000e+04         8.694900e+04         86949.000000  8.694900e+04   \n",
       "mean     5.884997e+04         2.302507e+05             2.131399  3.696228e+03   \n",
       "std      2.177511e+06         3.542068e+05             2.014193  3.981021e+04   \n",
       "min      0.000000e+00         0.000000e+00             0.000000  0.000000e+00   \n",
       "25%      1.650000e+04         0.000000e+00             0.000000  0.000000e+00   \n",
       "50%      2.500000e+04         1.000000e+05             2.000000  0.000000e+00   \n",
       "75%      4.000000e+04         3.000000e+05             4.000000  3.500000e+03   \n",
       "max      4.445544e+08         1.000000e+07            10.000000  1.000000e+07   \n",
       "\n",
       "       Loan_Amount_Submitted  Loan_Tenure_Submitted  Interest_Rate  \\\n",
       "count           5.240700e+04           52407.000000   27726.000000   \n",
       "mean            3.950106e+05               3.891369      19.197474   \n",
       "std             3.082481e+05               1.165359       5.834213   \n",
       "min             5.000000e+04               1.000000      11.990000   \n",
       "25%             2.000000e+05               3.000000      15.250000   \n",
       "50%             3.000000e+05               4.000000      18.000000   \n",
       "75%             5.000000e+05               5.000000      20.000000   \n",
       "max             3.000000e+06               6.000000      37.000000   \n",
       "\n",
       "       Processing_Fee          Var4      LoggedIn     Disbursed  \n",
       "count    27420.000000  87020.000000  87020.000000  87019.000000  \n",
       "mean      5131.150839      2.949793      0.029350      0.014629  \n",
       "std       4725.837644      1.697736      0.168785      0.120063  \n",
       "min        200.000000      0.000000      0.000000      0.000000  \n",
       "25%       2000.000000      1.000000      0.000000      0.000000  \n",
       "50%       4000.000000      3.000000      0.000000      0.000000  \n",
       "75%       6250.000000      5.000000      0.000000      0.000000  \n",
       "max      50000.000000      7.000000      1.000000      1.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 87020 entries, 0 to 87019\n",
      "Data columns (total 26 columns):\n",
      "ID                       87020 non-null object\n",
      "Gender                   87020 non-null object\n",
      "City                     86017 non-null object\n",
      "Monthly_Income           87020 non-null int64\n",
      "DOB                      87020 non-null object\n",
      "Lead_Creation_Date       87020 non-null object\n",
      "Loan_Amount_Applied      86949 non-null float64\n",
      "Loan_Tenure_Applied      86949 non-null float64\n",
      "Existing_EMI             86949 non-null float64\n",
      "Employer_Name            86949 non-null object\n",
      "Salary_Account           75256 non-null object\n",
      "Mobile_Verified          87020 non-null object\n",
      "Var5                     87020 non-null object\n",
      "Var1                     87019 non-null object\n",
      "Loan_Amount_Submitted    52407 non-null float64\n",
      "Loan_Tenure_Submitted    52407 non-null float64\n",
      "Interest_Rate            27726 non-null float64\n",
      "Processing_Fee           27420 non-null float64\n",
      "EMI_Loan_Submitted       27727 non-null object\n",
      "Filled_Form              87020 non-null object\n",
      "Device_Type              87020 non-null object\n",
      "Var2                     87020 non-null object\n",
      "Source                   87020 non-null object\n",
      "Var4                     87020 non-null int64\n",
      "LoggedIn                 87020 non-null int64\n",
      "Disbursed                87019 non-null float64\n",
      "dtypes: float64(8), int64(3), object(15)\n",
      "memory usage: 17.3+ MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有缺失值，但是决策树对缺失值可以处理，所以暂时没有做处理\n",
    "distbured 中有个缺失值，把那行删除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of occurrences')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 看下样本中正负样本数各多少\n",
    "sns.countplot(train['Disbursed'])\n",
    "plt.xlabel('Disbursed')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    85746\n",
       "1.0     1273\n",
       "Name: Disbursed, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Disbursed'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "样本类别数量差别好大，均衡一下样本，可以降采样，也可以设置类别权重，那就设置一下类别权重\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0924fcebe0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Disbursed\", hue=\"Gender\",data=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "不同类别中男女差别不大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Delhi                  12527\n",
       "Bengaluru              10824\n",
       "Mumbai                 10795\n",
       "Hyderabad               7272\n",
       "Chennai                 6916\n",
       "Pune                    5207\n",
       "Kolkata                 2888\n",
       "Ahmedabad               1788\n",
       "Jaipur                  1331\n",
       "Gurgaon                 1212\n",
       "Coimbatore              1147\n",
       "Thane                    905\n",
       "Chandigarh               870\n",
       "Surat                    802\n",
       "Visakhapatnam            764\n",
       "Indore                   734\n",
       "Vadodara                 624\n",
       "Nagpur                   594\n",
       "Lucknow                  580\n",
       "Ghaziabad                560\n",
       "Bhopal                   513\n",
       "Kochi                    492\n",
       "Patna                    461\n",
       "Faridabad                447\n",
       "Madurai                  375\n",
       "Noida                    373\n",
       "Gautam Buddha Nagar      338\n",
       "Dehradun                 314\n",
       "Raipur                   289\n",
       "Bhubaneswar              277\n",
       "                       ...  \n",
       "Jashpur                    1\n",
       "Fazilka                    1\n",
       "Chandel                    1\n",
       "DHORAJI                    1\n",
       "Sheopur                    1\n",
       "Gopal Ganj                 1\n",
       "Umaria                     1\n",
       "Rampur                     1\n",
       "RADHANPUR                  1\n",
       "Magadh                     1\n",
       "Dungarpur                  1\n",
       "Khagaria                   1\n",
       "Munger                     1\n",
       "Beawar                     1\n",
       "Narayanpur                 1\n",
       "Lakshadweep                1\n",
       "Seoni                      1\n",
       "Dantewada                  1\n",
       "Gadwal                     1\n",
       "Poonch                     1\n",
       "Shahpura                   1\n",
       "Muktsar                    1\n",
       "Lalitpur                   1\n",
       "Nalbari                    1\n",
       "SAYAN                      1\n",
       "Tamenglong                 1\n",
       "Himatnagar                 1\n",
       "Narsinghpur                1\n",
       "KHAMBHAT                   1\n",
       "Champawat                  1\n",
       "Name: City, Length: 697, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['City'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将数量小于1000的归为一类，用othercity代替"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lusha/.local/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Disbursed', y='Monthly_Income', data=train, hue=\"Disbursed\")\n",
    "plt.xlabel('Disbursed')\n",
    "plt.ylabel('Monthly_Income')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(train.shape[0]), train[\"Monthly_Income\"].values,color='purple')\n",
    "plt.title(\"Distribution of Price\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "决策树对这两个很离群的点应该不敏感吧\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#生日改成年龄\n",
    "train['Age'] =pd.to_datetime(train['DOB'])\n",
    "\n",
    "\n",
    "import datetime as dt\n",
    "\n",
    "train['Age']=2018-train.Age.dt.year #当前年份应该是2015\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Disbursed', y='Age', data=train, hue=\"Disbursed\")\n",
    "plt.xlabel('Disbursed')\n",
    "plt.ylabel('Age')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015    87020\n",
       "Name: Lead_Creation_Date, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Lead_Creation_Date'] = pd.to_datetime(train['Lead_Creation_Date'])\n",
    "\n",
    "train.Lead_Creation_Date.dt.year.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "都是发生在2015年的，所以不考虑2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7    32996\n",
       "6    27063\n",
       "5    26961\n",
       "Name: Lead_Creation_Date, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Lead_Creation_Date.dt.month.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#加个月份特征\n",
    "train['Lead_Creation_month']=train.Lead_Creation_Date.dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f092441ae10>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Disbursed\", hue=\"Lead_Creation_month\",data=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "和月份关系好像不大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Disbursed', y='Loan_Amount_Applied', data=train, hue=\"Disbursed\")\n",
    "plt.xlabel('Disbursed')\n",
    "plt.ylabel('Loan_Amount_Applied')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " 差别有些，负样本除了个别点比较大之后，其余值比正样本还小，居然没有被审批过关？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Disbursed', y='Loan_Tenure_Applied', data=train, hue=\"Disbursed\")\n",
    "plt.xlabel('Disbursed')\n",
    "plt.ylabel('Loan_Tenure_Applied')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0        58238\n",
       "5000.0      2695\n",
       "10000.0     1737\n",
       "3000.0      1581\n",
       "4000.0      1226\n",
       "2000.0      1097\n",
       "6000.0       837\n",
       "15000.0      800\n",
       "8000.0       786\n",
       "2500.0       727\n",
       "7000.0       718\n",
       "3500.0       601\n",
       "20000.0      521\n",
       "12000.0      480\n",
       "9000.0       361\n",
       "4500.0       340\n",
       "1500.0       323\n",
       "1000.0       323\n",
       "25000.0      282\n",
       "11000.0      277\n",
       "7500.0       255\n",
       "30000.0      241\n",
       "14000.0      217\n",
       "13000.0      209\n",
       "5500.0       203\n",
       "8500.0       187\n",
       "18000.0      185\n",
       "6500.0       166\n",
       "16000.0      158\n",
       "17000.0      158\n",
       "           ...  \n",
       "8320.0         1\n",
       "22300.0        1\n",
       "9359.0         1\n",
       "13161.0        1\n",
       "5801.0         1\n",
       "6348.0         1\n",
       "9370.0         1\n",
       "14062.0        1\n",
       "6918.0         1\n",
       "2855.0         1\n",
       "1989.0         1\n",
       "9387.0         1\n",
       "11434.0        1\n",
       "13481.0        1\n",
       "6802.0         1\n",
       "27250.0        1\n",
       "2858.0         1\n",
       "8326.0         1\n",
       "9676.0         1\n",
       "4105.0         1\n",
       "7762.0         1\n",
       "11427.0        1\n",
       "6737.0         1\n",
       "122.0          1\n",
       "2344.0         1\n",
       "11036.0        1\n",
       "1703.0         1\n",
       "2335.0         1\n",
       "2936.0         1\n",
       "1543.0         1\n",
       "Name: Existing_EMI, Length: 3753, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Existing_EMI'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这个特征就设为有为1没有为0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                               4914\n",
       "TATA CONSULTANCY SERVICES LTD (TCS)              550\n",
       "COGNIZANT TECHNOLOGY SOLUTIONS INDIA PVT LTD     404\n",
       "ACCENTURE SERVICES PVT LTD                       324\n",
       "GOOGLE                                           301\n",
       "HCL TECHNOLOGIES LTD                             250\n",
       "ICICI BANK LTD                                   239\n",
       "INDIAN AIR FORCE                                 191\n",
       "INFOSYS TECHNOLOGIES                             181\n",
       "GENPACT                                          179\n",
       "IBM CORPORATION                                  173\n",
       "INDIAN ARMY                                      171\n",
       "TYPE SLOWLY FOR AUTO FILL                        162\n",
       "WIPRO TECHNOLOGIES                               155\n",
       "HDFC BANK LTD                                    148\n",
       "IKYA HUMAN CAPITAL SOLUTIONS LTD                 142\n",
       "STATE GOVERNMENT                                 134\n",
       "INDIAN RAILWAY                                   130\n",
       "INDIAN NAVY                                      128\n",
       "ARMY                                             126\n",
       "WIPRO BPO                                        116\n",
       "OTHERS                                           115\n",
       "CONVERGYS INDIA SERVICES PVT LTD                 113\n",
       "TECH MAHINDRA LTD                                113\n",
       "SERCO BPO PVT LTD                                108\n",
       "IBM GLOBAL SERVICES INDIA LTD                    104\n",
       "CONCENTRIX DAKSH SERVICES INDIA PVT LTD           99\n",
       "RANDSTAD INDIA LTD                                96\n",
       "CAPGEMINI INDIA PVT LTD                           96\n",
       "ADECCO INDIA PVT LTD                              95\n",
       "                                                ... \n",
       "AVIVA LIFE                                         1\n",
       "RAK CERAMICS INDIA PVT LTD                         1\n",
       "JOHN VICTOR                                        1\n",
       "TATA AUTOCOMP SYSTEMS LTD                          1\n",
       "THATTVA INNOVATIONS                                1\n",
       "MSD ORGANON INDIA LTD                              1\n",
       "MOHANBROTHERSDRINKSPVTLTD                          1\n",
       "DAVENDER KUMAR                                     1\n",
       "KITS                                               1\n",
       "SARAVANAN S                                        1\n",
       "SHASHIREKHA                                        1\n",
       "HARESHMAKWANA                                      1\n",
       "MICROTHEREPEUTIC LABS                              1\n",
       "CTRLS DATACENTERS LTD                              1\n",
       "PARSVANATH OPTICAL                                 1\n",
       "GREEN WOOD ENTERPRISES                             1\n",
       "DONGSAN AUTOMOTIVE INDIA PVT LTD                   1\n",
       "BAJAJALLIANZ  LIFE INSURANCE COMPANY               1\n",
       "DISTRICT COLLECTOR OFFICE                          1\n",
       "VASTU REAL ESTATE                                  1\n",
       "K SHESHKMAR                                        1\n",
       "ZOOM TECHNOLOGIES INDIA PVT LTD                    1\n",
       "MRS.SNEHAL THAKUR                                  1\n",
       "SNEHA MAHILA VIKAS INSTITUTE OF HOTEL MG           1\n",
       "TPF SOFTWARE INDIA P LTD                           1\n",
       "ISOURCE                                            1\n",
       "MAHI DEVELOPERS                                    1\n",
       "EAGE EDUSOLUTIONS                                  1\n",
       "JK TYRES & INDUSTRIES LTD                          1\n",
       "IDBI INTECH  LTD                                   1\n",
       "Name: Employer_Name, Length: 43567, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Employer_Name'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这个特征有为1，没有为0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f09242f4e48>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Employer_Name_Missing'] = train['Employer_Name'].apply(lambda x: 0 if x=='0' else 1)\n",
    "train[['Employer_Name_Missing','Employer_Name']].head()\n",
    "sns.countplot(x=\"Disbursed\", hue=\"Employer_Name_Missing\",data=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "没有employername基本没发放贷款，这个特征有为1，没有为0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Interest_Rate')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Disbursed', y='Interest_Rate', data=train, hue=\"Disbursed\")\n",
    "plt.xlabel('Disbursed')\n",
    "plt.ylabel('Interest_Rate')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有差别"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HDFC Bank                                          17695\n",
       "ICICI Bank                                         13636\n",
       "State Bank of India                                11843\n",
       "Axis Bank                                           8783\n",
       "Citibank                                            2376\n",
       "Kotak Bank                                          2067\n",
       "IDBI Bank                                           1550\n",
       "Punjab National Bank                                1201\n",
       "Bank of India                                       1170\n",
       "Bank of Baroda                                      1126\n",
       "Standard Chartered Bank                              995\n",
       "Canara Bank                                          989\n",
       "Union Bank of India                                  951\n",
       "Yes Bank                                             779\n",
       "ING Vysya                                            678\n",
       "Corporation bank                                     649\n",
       "Indian Overseas Bank                                 612\n",
       "State Bank of Hyderabad                              597\n",
       "Indian Bank                                          555\n",
       "Oriental Bank of Commerce                            524\n",
       "IndusInd Bank                                        503\n",
       "Andhra Bank                                          485\n",
       "Central Bank of India                                445\n",
       "Syndicate Bank                                       415\n",
       "Bank of Maharasthra                                  406\n",
       "State Bank of Bikaner & Jaipur                       331\n",
       "HSBC                                                 328\n",
       "Karur Vysya Bank                                     326\n",
       "State Bank of Mysore                                 255\n",
       "Federal Bank                                         253\n",
       "Vijaya Bank                                          252\n",
       "Allahabad Bank                                       238\n",
       "UCO Bank                                             237\n",
       "State Bank of Travancore                             227\n",
       "Karnataka Bank                                       200\n",
       "Saraswat Bank                                        195\n",
       "United Bank of India                                 183\n",
       "Dena Bank                                            182\n",
       "State Bank of Patiala                                177\n",
       "South Indian Bank                                    160\n",
       "Deutsche Bank                                        125\n",
       "Abhyuday Co-op Bank Ltd                              108\n",
       "The Ratnakar Bank Ltd                                 83\n",
       "Tamil Nadu Mercantile Bank                            71\n",
       "Punjab & Sind bank                                    66\n",
       "J&K Bank                                              59\n",
       "Lakshmi Vilas bank                                    50\n",
       "Dhanalakshmi Bank Ltd                                 42\n",
       "State Bank of Indore                                  18\n",
       "Catholic Syrian Bank                                  14\n",
       "India Bulls                                           11\n",
       "GIC Housing Finance Ltd                                8\n",
       "B N P Paribas                                          8\n",
       "Firstrand Bank Limited                                 7\n",
       "Bank of Rajasthan                                      5\n",
       "Kerala Gramin Bank                                     4\n",
       "Industrial And Commercial Bank Of China Limited        2\n",
       "N                                                      1\n",
       "Name: Salary_Account, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Salary_Account'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "大于1000为1，小于为other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    56481\n",
       "N    30538\n",
       "0        1\n",
       "Name: Mobile_Verified, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Mobile_Verified'].value_counts()\n",
    "#sns.violinplot(x='Disbursed', y='Mobile_Verified', data=train, hue=\"Disbursed\")\n",
    "#plt.xlabel('Disbursed')\n",
    "#plt.ylabel('Mobile_Verified')\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有一个缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.violinplot(x='Disbursed', y='Mobile_Verified', data=train, hue=\"Disbursed\")\n",
    "plt.xlabel('Disbursed')\n",
    "plt.ylabel('Mobile_Verified')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       17814\n",
       "0       11272\n",
       "1        7536\n",
       "3        4966\n",
       "1        4700\n",
       "11       4003\n",
       "2        2974\n",
       "14       2479\n",
       "15       2223\n",
       "12       2208\n",
       "13       1795\n",
       "3        1793\n",
       "2        1511\n",
       "8        1459\n",
       "10       1326\n",
       "16       1324\n",
       "4        1292\n",
       "15       1286\n",
       "11       1201\n",
       "14       1183\n",
       "9        1175\n",
       "9        1106\n",
       "10       1101\n",
       "8        1056\n",
       "7         981\n",
       "17        936\n",
       "13        827\n",
       "12        781\n",
       "16        773\n",
       "17        755\n",
       "6         685\n",
       "5         639\n",
       "4         523\n",
       "7         508\n",
       "5         336\n",
       "6         298\n",
       "18        194\n",
       "HBXX        1\n",
       "Name: Var5, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Var5'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HBXX    59293\n",
       "HBXC     9010\n",
       "HBXB     4479\n",
       "HAXA     2909\n",
       "HBXA     2123\n",
       "HAXB     2011\n",
       "HBXD     1964\n",
       "HAXC     1536\n",
       "HBXH      970\n",
       "HCXF      722\n",
       "HAYT      508\n",
       "HAVC      384\n",
       "HAXM      268\n",
       "HCXD      237\n",
       "HCYS      217\n",
       "HVYS      186\n",
       "HAZD      109\n",
       "HCXG       78\n",
       "HAXF       15\n",
       "Name: Var1, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Var1'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Loan_Amount_Submitted')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Disbursed', y='Loan_Amount_Submitted', data=train, hue=\"Disbursed\")\n",
    "plt.xlabel('Disbursed')\n",
    "plt.ylabel('Loan_Amount_Submitted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Loan_Tenure_Submitted')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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L2bRpE+vWrePb5cs5kL8c8kFHd6IxvlvTQK4kr8o/yl6PsbLg54FSTbNlnnxyLjk5OQwaNIjMzMyA/tUXItiys7N57vkXeP7551m4cCGrS61ckFnLmekNGDvwCFytYXlRBP/dGcvBBjj33HOZNWsWsbGxQYnHm4Rfq5RKomkQllIqF6jyS1TtRFJSEqeddhqnnXYas2fPJj8/n+XLl7Ns2bds3rwOve8HXNGdaOg6DGd8xgkTv7LVELH/RyJKt4F2kZSczGmTzmPUqFEMHTpUpkMWYc9isfDHP/6RCRMmMP+xR3nl+7V8vT+SGb1qyE62Bzs8r+2oNPHq9hi2Vxrp27cP9157XdDXt/Z4aoWmBP8IMBD4EUgHpmqtf/BVMO1pPvyWHDx4kKVLl/LKK69SXFyEKyaVhvThROxbh6n650EUzugUnNGdiCjdilHBpEmTmDRpEr1795abrEIch9aab7/9lv97fD779h9gSLKdi3vX0DXaN0WF+9bGNRtp2y/Bzm3DfXP9Wt5g4L87olheZCEpMYH/vfoPnHPOORgM/vuo4vO5dJRSJsAA9AcU8BPg0lr77A5LR0r4h9jtdhYtWsR//vMSZWWluCyxGGzVP++gDBgUTJgwgUsvvVTmthHCC42Njbzzzjv858UXsTXUc263es7vWYeljRVPfyR8hws+3Wvl3d3RuAwmpk+/iBkzZgTk/ps/Ev7ao+fNOdZ7bdERE/4hjY2N/OMf/2Dp0qXN3jcYjDzzzNP06tUrSJEJ0fFVVFTw1FNPsWjRIpIj4ZLeVYxIaX2Zx9cJf0uFiRe3xlJYY2DM6NHMmj2bLl26tLo9b3ma8Fv8jKGUSlVKDQEilVLZSqnBTY8xgHQdaRIREcEdd9zxi5GtvXplSbIXoo0SEhK4+eabeeyxx4hPy+TfG+J4Pi8aW5BnDnG4YOGOSO5bG48zujP33Xcf9/7znwFN9t7w5KbtRNwjajOAx3GXc8B9w/YOP8XVIUVERNC1a1e2bt16+D3pTimE72RnZ/P0s8/y/PPPs2DBa2yttHDdoAq6+Ki2740Km+LRjfFsrzQyYcIErrnmmnb/++7J5Gkv4J5S4Sat9YNHblNKtX16yxATGRkZ7BCECGkmk4mrr76aESNG8I+7/8596xS3DK0I6MybB22K+9clctBh4c47b2H8+PEBO3dbeHPb+KJjvPeurwIRQghvjBgxgn8/Nh9jVAL/WpdAuS0wHfbrHfCvdYlUOK08+NCcDpPswbMafh+l1G+AeKXU5CMelwAdZNYxIUQo6tGjB/Me+TcNLjMLtgWmnPLe7igO1Cru+9f9DB48OCDn9BVP/iQOxL0ISgIw9YjHKOAP/gtNCCFa1qNHD2b87nesLLawp8a/I9SrGhWf7o3k3HPPZdiwYX49lz94UsN/B/cUx2O01ssCEJMQQnjlV7/6FS+++CK7q0x0j/FfLX9vjRGnDtz89b7myZq2N2qt5wK/VUpdcPR2rfUNfolMCCE8lJSUBEBFo3/r+IfaT0xM9Ot5/MWTbpk7mv7d6M9AhBCitX766ScAMmP9O7VyZqzz8PmysrL8ei5/8KSk827Tv8/5P5yOz+EI3bm8hWiPtNa8/voCIs3QJ96/k6x1jXKSGqV5682FnHvuuZhMHWvFFo8//yilhiulFiqlViml1h56+DO4jujo1edlEXEh/Ovrr79mzZrvuSCz1u8rZikFF/eqZtfufBYuXOjfk/mBN9+eBcCtwAYgZObB96WvvvqKsrLmKz/u2bOH+vp6GZAlhB9s3bqVBx64n17xTs5MbwjIOYd3spOT0sgzTz9NVlYWI0eODMh5fcGbOxylWuu3tdbbtNY7Dj38FlkH4nQ6eeqpp7jnnnvQRnOzbVVVVfzxT7PYv39/kKITIjQVFhZy6y03E2No5LpBlZgCtFCKUnB1/2oyYpzc/fe7yMvLC8yJfcCbb9HdSqknlVJTjxyA5bfIOgCbzcZHH33E5ZdfwYIFC2hM6YczMqnZPs7IRPL3FvI//3M5jz76qCR+IXzgwIEDXP/n67DVHOTG7AoSLIFdDtFqghuyK4lW9fzlxhvYtm1bQM/fWt6UdH4HDAZi+bmko4H3fR1Ue1dRUcGHH37IwjffpLKiAh2dTEOvcTiSehKZ93GzfbXJQnXvM7HsW8fb777LO++8w+mnn860adPo37+/LIIihJeKi4u5/s/XUVNRyi1DK8jwY7/7E0myurhlSAX3rVPceMP1PPLvR9t9zx1vEv4pWuu+fouknSsrK2PZsmUs/uYbfly3DpfLhTM+A1vfU3DGdjnh8obaGkdD1lhsjSMwF/3EN8uWs3jxYtLSOjNu3BmMHTtWkr8QHigvL+eG6/9MRWkRNw2tPNxNMlhSIl3cMuQg962DG67/M48+Np/u3dvvnJLeJPyVSqm+WustfoumnSkqKmLZsmV8vXgxmzZuRGsNkQnY0gbhSOqFK8q7wRc6IprGbrk0dh2CuXw3hQd38/p//8vrr79OcqcUzjh9LKeffjoDBw6URcyFOIrNZuO2W2+h5MB+/jqkkl5x7aMLdFqUi5ubkv7NN/2VJ558ioSEhGCHdUzeJPxhwHql1HbAhntefO3LFa+CTWvNzp07WbZsGUuWLmPHdnddTkcl0dhlKI6kTFzWhBNezXvEGIE9pQ/2lD7gsGGq2MuBg7t5+513eeutt4iNi2fM6FGMGTOGnJwcWeBcCODBBx9ky5atXJtdRZ+E9pHsD+ka7eLPgyr51w9w5x23M++Rf7fLizZvEv75rT2JUsoIrAEKtdaTWtuOv+zYsYNFixaxZMlSiouLAHDFpNKYkYsjsTvaGu+/k5ssODr1xtGpNw1OO6bKAuwH8/nk869YtGgR5ogITs49mXHjzuCMM87ocAM9hPCF5cuX8+WXX3JBz7o2LW3oT73jHVzRt5qnN2zk/fffZ8qUKcEO6Rc8zh5a6x1KqcHAGNw3a7/VWq/38PDrgM1AnPch+ofWmnXr1vHaawtYvXoVymDEHtsVR+ZoHAnd0eYg9Js3mnEk9cSR1JMGlxNj9QFMFXv4ds0PfPvtMp548ikumj6NiRMntvuVdYTwFZfLxWOP/pv0GBeTetQHO5wTGt25kW+L7Dz7zNOcc8457e731JuRtn/DPfgqHfdyh68ppW714LgM3MskPtvaIH1t165d/GHmTK6//nrW/LgBW/oIqoZcRH2fs7Cn9A1Osj+awYgzPh1bj1Opzp5G3UlnUWwz8vjjjzN16jTeeuutYEcoREBs3bqV/QeKmNitNmB97VtLKfhNjzpq6+pZs2ZNsMP5BW++fZcBuVrrv2mt/wacDFzuwXGPADfRjkbnLliwgK3bd9LQYxRV2VNp7DoETD6qkzsbsVqtXHjhhVitVnA2tr1NpXAmdKOu33nU9p9ElYriscceo6EhMCMLhQim9evdhYTsZN+Vcuodqtnvab3Ddz3kesc7sBjhxx9/9FmbvuJNwt9P8xKQqem941JKTQKKtdbfn2Cfq5VSa5RSa0pKSrwIp/X2HziAyxyFPbkXGHxbE1eORiZNmsTs2bOZOHEiyuGDhH8EV3QKjvgMwN2LSIhQd+i+lcGHvZbrHKrZ72mdDxP+oU8h7fF+myfz4c/DXbMvBzYppT5ten02sLqFw0cDk5VS5+FeDjFOKfWK1vqSQztorZ8GngbIyckJyHC5k3qfxIb164nbsJD6tGzsqf3gqCkRWkubIvjwww/RWvPRRx+hTb6r4Rmr9mPdtxZDdRGdUlKIi2s3t0SE8JtOnToBkF9tYlCSb67yo0y62e9pmsl3qWd/nQGb8+e42xNP/gQdmgd/E/DREe+vaOlArfWtuCdcQyl1BvCXI5N9sFx77TWceeZ4nn/hBb5fs5rI4o3YErNwJPbAGZMKqg2FQmMEDXXlP9fYY9vWH1fZajBV7MFcvhNjTTGJSclcdt11TJw4kYiIiDa1LURHMHLkSOLjYvlsb6PPEn6kSdNQ03D49zQywXcJ//MCKyajsV0ubu7JfPghOQ/+wIEDmTtnDhs2bOC1115j1erVOIs2oSIiscV1cyf/uK5gCHxfWkN9BaaD+Zgr8jHUlgLQrVt3zr/iGiZNmiT98kVYsVgsXDh1Gs899xzLD0QwqrNvy6S+lHfQxJeFkZx33rkkJycHO5xf8LjIpJTahruU04zWuo8nx2utFwOLPT1foGRnZ/Ovf/2L2tpaVq5cydKlS/nuuxU0lG5FmSJojEvHkZjprpv7qOzzC1pjqC3FVJGPpWIP1FcA0LdfP04fewFjxoxp18O1hfC3iy++mJUrVvDilp/oGn0w6FMqHEtJvYEnNsfTtUsXZs2aFexwjsmbuwpjjnhuBaYCfhyRFFjR0dGMHz+e8ePH09jYyNq1a1m6dClLli6jescuMBixx6XjSOyBI6EbmKxtO6F2YawuwnQwn4jKPWCrwWAwMGToUE4fO5bRo0eTkpLimy9OiA7OZDJx5113MXvWn3jwR80tQyro3o6SfmmDgfvXJWA3RnHPP+5td/3vD1Fat752pZRao7XO8VUwOTk5ur31XXU6nWzcuJElS5aw+JsllJWWgDJgT+xBY9pAXDGpzfaPzPsYU/XPq145YjtT3++8w6+VvR5zyRYspVvAVovZbCY3N5exY8cyatQouRErxAkUFhZy3bXX0FBdzvXZlZwU37opFu5bG0dexc+f2Psl2LlteFWr2tpXa2DO+gTqVRRzH55Hv379WtVOWyilvvckF3tT0hl8xEsDkAOEfDHZaDQyZMgQhgwZwuzZs9myZQtffvklH370EfWbd+GKScGWOgBHYk8wHP9mr6GuHHPRJizlO9EuJyNycvj1pEmcfPLJ7fZqQIj2Jj09nX8/+hg3/fUv3L8O/jSgKqhTLWytMPHIxnhMkXE8/OBD9O3bvicU9qak8/gRzx3AbmC6T6Np55RS9OvXj379+nHFFVfw6aefsvDNN9m38xt09EZqs8745UFaY9m7mogDG4iIsHDupIlccMEFZGZmBjh6IUJDeno6j//fE9x6y808umELF59UyzkZDW2e09BbK4oieGZzLKmdu/DQnLl07do1sAG0QptKOr7WHks6LXG5XCxdupQHH5pDXX0DDnMUxobKw9u1wYxy2Zk8eTK///3vpWQjhI80NDTwz3vvZemyZfwqvYFL+tR6PDirLSUdreHDfCsLd0aTPWgg9/7zPuLjg3s709OSTosdzpVS5ymluh/x+jal1PdKqbeVUj3aGmhHZzAYOP3003nh+efo37dPs2QPYNBO7rrrLm644QZJ9kL4kNVq5e577mHatGl8UWjl8Y0x2P08gYtLw6vboli4093JY+7D84Ke7L3hyQijfwFlAEqpicCVwJ+Az4Cn/Bdax5Kamsq8eQ//YjBUz56ZjBs3LigxCRHqDAYDf/rTn/jjH//I6hILD6+Po9FPnXdcGp7ZHM1nBZFMnTqV22+/vcMNfvQk4WutdW3T8wuAZ7XWK7XWTwJp/gut47FYLGRkZDR7LzY2NkjRCBE+pk+fzs0338ymcjP/tykWp4+v9LWGV7ZG8+0BK1deeSV/+tOfMJygk0Z75UnEBqVUlHIvuHom8NUR20K+l463JMELERwTJkzg2muvZW1pBK/v8G3Pt08LrHxRaGX69OlceumlHXb9aU8S/mPAD8BKYJvWehWAUmoIcOBEBwohRCBdcMEFnH/++Xy6N5KN5b4ZGV9QY2ThjmhGnXoqM2fO7LDJHjxI+FrrZ4CzgFnAuUdsKsVdzwdAKRX40QZCCHGUmTNnkpHelVe2xeKLTogLtkcTFRPLX2+6qUMne/BwPnyt9R6t9WqttfOI9wq11ruP2O01XwcnhBDeslqtXHLpZeyrVWw+2LY56YvqDGwoN/PbC6eSmJjoowiDx5d3HTr2nz4hRMgYN24cZpORDeVt60WzoaksdNZZZ/kirKDzZcJvPyO4hBBhzWKxkJLSidKGtqW4sgYjJqORtLTQ6JDY8foVCSGEBxwOJ8Y21h0MSqPRuFztZknuNvFlwm8/c5UKIcJadXU1xSWldIlqW1rqGuXE6XSxe/du3wQWZF4lfKXURUqpvzU976aUGnFom9Y619fBCSFEa3zSMsyzAAAgAElEQVTxxRcADE5u2+pYA5PsGNTP7XV0Hid8pdR8YBxwaE3aWuBJfwTVkdXV1QU7BCHCmsPh4M2FC+kR66JnXNuu8BMsmmGdbHz04QdUV1f7KMLg8eYKf5TW+g9AA4DWuhzoWBNJ+JnWmuLi4mbv2e3Bm6tbiHD07rvvUrhvH7/tWeOT9s7PrKemppaXXnrJJ+0FkzcJ366UMtDUG0cplQyExp0MH3nvvfeorGw+W+auXbtobGy/iy4LEUqKiop4/rlnGZRkZ0iyby62esQ6Ob1rA2+99RZbt271SZvB4k3Cfxx4C0hRSt0NLAMe8EtUHYzL5eKNN97g0UcfQxubf+ipr6/npptuoqysLEjRCREetNbMmfMQTruNK/rW+HRBlOm96oiLcHH/ffd16E/tHid8rfVLwO3AHOAgMFVr/bq/AusoKioquPXW23jiiSdojM/AGZnQbLvTGs+PGzZyxZVXsXr16iBFKUTo++ijj1i9eg3Ts6pJifRt8SHarLmiTxU7d+/u0KUdjxK+UsqolNqktd6ktf631voRrfVGfwfXntlsNhYsWMDFM2awcvVqGrqfQn2v8aCaf0u1OZKa/r+m0q7461//yu23386ePXuCFLUQoamkpIT/e3w+/RMdjE+3+eUcwzrZGd25gVdffZVt27b55Rz+5ulcOk5gp1Iq3c/xtHtOp5PPPvuMGb+7hKeeeopqczK1AyZjTxvA8T5DuiITqen3a2zpw/n2u5VcfvnlzJs3j/Ly8gBHL0RoeuKJJ7DbGriyb7XHyxy2xiUn1RFtcvHvRx6hPS0P6ylvZhaKATYrpb7D3SUTAK31BT6Pqh3SWrNq1SqeePIpdu/aiSu6Ew19J+CM6+JZA0YTjV2HYk/pS8S+H3jv/ff55NNPufiii5g2bRpRUb6dv1uIcLFlyxa++uorfpNZR1qUf/uRRJs107JqeG7TJpYtW8Zpp53m1/P5mjcJ/15vG1dKWYEluBdKMQFvaq3v8radYMvPz2fevHmsW7cOrHHUZ52BI6nnca/oT0SbI7H1GEVj2kDsBd/z4osv8vY773L1//6eiRMndvjpV4UItIULF2I1wYTuDQE532ldbLyfH83C/74Ruglfa/1lK9q3AeO11jVKKTOwTCm1SGu9ohVtBcXatWv52+2302B30dB9JPaUfmAwtrldbY2nofd4GmuKcRasYc6cOWzbto1rrrkGk6ltU7oKES4aGxv55pvFjE1rIMoUmBKLQcH4rnW8sWEjRUVFHWpiNW9G2lYrpaqaHnVKKZtSqupEx2i3Q6MfzE2PDlP4WrZsGX/5y1+pw0J1/8nY0wb6JNkfyRWTSl3fCdg6Z/Pee+9x5513dcjaoBDBsG3bNux2B4OSAjvWZWCSu2vmxo0dq++KN1f4hxdrbRqAdQEwtKXjlFJG4HugN/C41nplK+IMik8//RSXyUJN34lg8uOgYqVo7JaL0i6WL/+WyspKEhISWj5OiDBXVFQEQJqPu2G2pHOke8qGo0fWt3etmi1Ta+3SWr8JTPRgX6fWeiiQAZyslBp05Hal1NVKqTVKqTUlJSWtCcdvoqOjUehW1erbel4hRMuMRvcnbleAPxQ7tWp2/o7Cm5LO5CMe5yul7gU8/hylta4Avqb5urhorZ/WWudorXNSUlI8DjwQTj31VLA3EJP3EarhhNWrttEuLHtWEVG0iZzcXMxm3yy+LESoS0939xTPrwnsfa89Ne5En5GREdDztpU3V/hTj3j8BrA3/XtcSqkUpVRC0/NI3Iuh57Uu1MA7/fTTefDBB4lRjcRu/gBz8WZwnXj2PVdUEtpoRhvNOGI744pKOuH+hpoSorYsIqJoI+effz7/uu8+X34JQoS0rKwsEuJi+aHUu4uk7jEOIo0uIo0u+iXY6R7j8Or4taURGAwGhgwZ4tVxweZNDf/SVrTfBfhPUx3fAPxXa/1hK9oJmtzcXJ599hn+9a/7Wb/+O6xFm6jvOgxHUtYxSz227qdgqHMPqKrvd95x21X1lVgKv8d8cDexcXFcc9ttnH322X77OoQIRQaDgfMm/ZoFr73G/to6ukR7Vsu/pE8de5o+Fdw23LtP79V2xeL9kYwbN67DlV89TvhKqU7AlUDmkcdpra8+3jFa6/XAsDbE1y506dKFf//7EVatWsWTTz3Frp3foIs2Up+RizOuq1dtKXs9EYVriSjdisVi4eLLL5eBV0K0wbRp03j7rbd4dXsMNw6u8vstt7d2RtHoVFxyySUt79zOeFP4eg9YgXuWzLBbzlApxciRI8nNzeXLL7/k6WeeoWTLJzgSumHLyMUV2UKvGpeDiAObsBZtQLmc/Ob887nssstITEwMzBcgRIhKSEjg6j/8gUcffZSl+y2M7eqfuXQANpab+arQyrRpU+nZs6ffzuMv3iT8aK31jX6LpIMwGAycddZZjB07lrfeeouXX34F06Z3acjIcffTP8blhaGunOidX0N9JaeOHs0fZ86kW7duQYheiNB0/vnn883ixby8aT1ZcQ4yYnx/TVphUzy1OY5uGelcddVVPm8/ELy5abtIKSVF5iYWi4UZM2awYMFrjBk9CuveVVh3fA3O5nNlm8p2EJP3IQkWA3PnzuW+f/5Tkr0QPmYwGLjjzjuJio3n0U3x1Dl8W9dxuGD+pjgaiOCef9yLxWLxafuB4k3Cnwl8opSqUUqVK6UOKqXCfrrHhIQE/vGPf/CHP/yBiIp8orZ/eXgssalsJ5E7v2HggP4899yzjBgx4sSNCSFarVOnTvz97nsoaTDyxKYYn/bNf3lrNFsrTNx0080dspRziDcJvxPuqRHigZSm1+2r43yQKKW4+OKLufHGGzFW7cPQWINyOYnas5z+AwbwyLx5JCcnBztMIULekCFDuPba6/ixLIK3dkb6pM2vCi18vc/KjBkzOPPMM33SZrB4s+KVE3cf/JubnnfBg6kVwsnEiRMZO/Z0DI01GBoqsZiM3HnHHTIZmhABNHnyZCZOnMgH+VF8X9K2QYw7Kk28vC2Gk3NzO2zd/kjejLSdD4wDDvXHrwOe9EdQHZVSihkzLnY/dzbyq1+dSZcuHs6XL4TwCaUU1157LX379uGZvDjKGlo1gwx1DsXjP8WTkpLK7Xfc0eGmUTgWb74To7TWfwAaALTW5YAfZxTrmPr27Xv4+ahRo4IYiRDhy2KxcOedd+EyWHhmc2yr6vmvbo2ivEFxx513ERcX5/sgg8CbhG9vmiVTAyilkoHATlHXARy5gEn37t2DGIkQ4S09PZ0/zZrFTwdNrCr27tp0W6WJpQesXDxjBgMHDvRThIHnTcJ/HHgLSFFK3Y17ANYDfokqRHTq1CnYIQgR1iZOnEhWz0wW7ozB4cXl6es7oklKTOiQo2lPpMWEr5QyAWitXwJuB+YAB4GpWuvX/Rtex9ZR++oKESqMRiNXXHkVJfWK9WWe3cDNrzayrcLEjN9dQmSkb3r6tBeedB9ZBQwH0FpvAjb5NaIQIuvTChF8p556KokJ8Xx7wMbwFHuL+y8/YMFsMobkZIaelHQkawkhOiyTycSInFy2V3v2iXtblZn+/QeEzI3aI3lyhZ+ilLrheBu11g/7MB4hhPC53r1788UXX1BrV0SbT9xlp7DOzDm9egUossDyJOEbgRjkSt9rDodDBl0J0Q4cqsXbPbhx2+jUHW6ee095ko32a63v8XskIai6ulqmPxaiHXA43CtaGTy4bDUosNtbrvV3RD6r4SulJLMdpbS0NNghCCGAffv2YTEpYlso5wCkWDX79+8PQFSB50nC93S2oC/bEkioaGz8eV33LVu2BDESIcQhW/LySI9yeLQaVkZ0I3mbf0JrH0632U60mPCbplDwhNT4gXXr1h1+vnLlyiBGIoQAqKurIy8vj/4JjS3vDPRLsFNSWkZhYaGfIwu81s0qdGyh9+fQS1prXn75FVAGXOYoli5bxu7du4MdlhBhbdWqVTicTgYne5bwBye76/fffvutP8MKCl8m/LC3aNEiNmxYjysiGh0RgzKYmDN3brMyjxAisL7++mviLNA3weHR/qmRLjLjXHz99Vd+jizwfJnww7qk88MPPzBn7lyccV1xmaPQBgN1PUazccMG5s6dG5L1QCHau7q6OlZ8t5zcTg0e9dA55OSUevLytoTczVuvEr5SyqiU6qqU6n7occTmjr0UTBusXLmSW265FWdELHW9xh1eyNyRnIWt6zA+/fRTHnzwwcNdw4QQgbFixQpsjXZOSbN5ddzIVPen8sWLF/shquDxZgGUa4Ai4HPgo6bHh4e2e3FzN6R88skn3HrrbTSYoqntcy6Ymg/fbuw6FFvXoSxatIjbbruN+vr6IEUqRPhZunQpcRY4Kd67i62UprLO0iXf+Cmy4PDmCv86oK/WeqDWOrvpMdhfgXUEr7/+Ovfffz/2mDRq+k5AR0T9cielaEwfTkOPUaxavZrrb7iBqqqqwAcrRJhxuVysWb2KIYnelXMOGZrUwOa8LdTU1Pg+uCDxJuHvBSq9aVwp1U0p9bVS6iel1Cal1HXehdd+PfPMMzz55JPYE3tSd9JZYDzxAgv21H7U9xrHli1bmTX7GsrLw/IDkRABs3v3bqpraumX2LpSav9EO1prNm7c6OPIgsebhL8TWKyUulUpdcOhRwvHOIAbtdYDgFOAWUqpAa0Ntr1YuXIlr776Ko2d+tDQ63QweLbWpSMxk9qTzqagoJB58+b5OUohwlt+fj4APWJbl/B7xDoBQqprtTcJfw/u+n0EEHvE47i01vu11mubnlcDm4H01oXaPtjtdubOfRgiE7D1OBWUdx2dnHFdaOg6hKVLl/Ldd9/5KUohxIEDBwBIsTpbdXyUSRNtVofbCQUeT+Wotb67LSdSSmUCw4CVR71/NXA1dIw1YCsqKiguLqKh20iPr+yP1pg2EEvB92zevJlTTz3VxxEKIeDnCdAsrfs1BSDCGFoTqXmc8JVSKcBNwEDAeuh9rfV4D46Nwb0e7p+11s3uWGqtnwaeBsjJyWn3ndWTkpIwGI0YGlt/I8dgcx+blpbmq7CEEEcxGt2Z3u5qfdJ3aDAYQmd8qjdfyatAHtATuBvYDaxu6SCllBl3sn9Va/12K2JsV4xGI6eecgoRJZsxVrfio57LQeTupVgsVoYNG+b7AIUQAKSkpABQ1tC6hG1zQrVNh9SFmTffiWSt9XOAXWv9jdb6SuCEV/fKvajrc8DmUFoZ65ZbbqFrly5E7/gKQ12Z5we6nFh3LsFQU8Ltt/+Nrl27+i9IIcJcjx49ANhT07pFiA4d1xFKzZ7yJuEfKmTtV0pNVEoNA5JaOGY0cCkwXim1rulxXmsCbU9iY2N58IEHSIqLISbvY4yVBS0f5LARvfVTzAd3M2vWLE477TT/BypEGOvVqxdRkVZ+Omhu1fGbm44bPDh0hht5k/DvVUrFAzcCfwGeBa4/0QFa62Vaa6W1Hqy1Htr0+LgN8bYbGRkZPPnkE2T26EbUts8xlW4/7r6qsZbYLR9jri/lzjvvZOrUqQGMVIjwZDKZyD15JGvLrDg8WNrwaKtLrPTv15eEhATfBxckHid8rfWHWutKrfVGrfU4rfUIrfX7/gyuvUtJSeHx+fMZOnQokbuWYC7O+8U+qqGKmC2LsOoG5s6Zw/jxLd7jFkL4yFlnnUWVDTaUe3eVv6fGSH61gbPOPsdPkQWHN3PpZCil3lFKlSilipVSbymlMvwZXEcQFRXFA/ffzymnnII1fzmmsp0/b3TYiNn2KdEmzSPz5jF06NDgBSpEGDrllFNITkzgy8JIr477ssBKhNnMmWeG1pyQ3pR0XgDeB7oAXYEPmt4LexaLhXvvvZcBAwYStWc5yuUADZG7l2Gw1/HQgw/Qr1+/YIcpRNgxmUxMPn8K68vM7K/1LN3V2BXLi6yMP/NM4uPj/RxhYHmT8FO01i9orR1NjxeBFD/F1eGYTCbuvPMOIi1mDA3VKEcDpoP5XP2//8uAAR1+NgkhOqzJkydjNpv4ZK9nV/lfFVqxOWHatGl+jizwvEn4ZUqpS5rmxDcqpS4BvOiTGPo6d+7Mhb/9Lcppw2CrJik5mQsvvDDYYQkR1hITEznnnHNZVmSluvHE02baXfBFYRQ5OSPIysoKUISB400H1SuBx4B5uNevXQ5c7oeYOrRJkybx0ksvobST8yZMwGRqXR9gcWJ2u52CggIaGhqCHYrfWK1WMjIyMJtb161Q/Gzq1Kl8+OGHfFVo5Tc9j78mxaqiCCpsMG3a9ABGFzjezKWTD0w+8j2l1J+BR3wdVEeWmpp6+Hl2dnYQIwltBQUFxMbGkpmZiVKht7qm1pqysjIKCgro2bNnsMPp8Hr06EHOiBEs/ul7fp1Zf9z58b/cF0W3jHRyc3MDG2CAtHWSiJamRw5roTRCr71paGggOTk5JJM9gFKK5OTkkP4EE2i/njyZsvrjd9EsqDGyvdLI5N+cH7I/V21N+KH5XfGRULvD396E6i/lIaH+9QXaqFGjiI6KZFXRsRcrWlUcgVIq5LpiHqmtCb/dz24ZTJGR3vX9FW1jNBoZOnQoAwcOZMiQIcydOxeXyz3Ecs2aNVx77bXHPXbx4sVMmjQpUKH+wt///nfmzJkTtPOHA7PZzKjRY/ih3IrrGJlrbZmV7OxBJCW1NGNMx9ViDV8pVc2xE7sCJKOdgFyhBVZkZCTr1q0DoLi4mBkzZlBVVcXdd99NTk4OOTk5fju3w+GQG/QdwIgRI/j8888prG0+X3KtXbG32sCvckKzdn9Ii1f4WutYrXXcMR6xWmv5CRftUmpqKk8//TTz589Ha93sCv6bb75h6NChDB06lGHDhlFdXQ1AVVUVEydOpG/fvsycOfPwp4OYmJjD7b755ptcfvnlAFx++eXMnDmTkSNHctNNNx233Yceeojc3FwGDx7MXXfddbitf/7zn/Tp04cxY8awZcuWQHxbwt6gQYMA2FnVPHXtrjaiIeTHzEjCFiErKysLp9NJcXFxs/fnzJnD448/zujRo6mpqcFqda/ns2rVKn766Sd69OjBueeey9tvv93iOIqCggKWL1+O0Wjk17/+9S/a/eyzz9i2bRurVq1Ca83kyZNZsmQJ0dHRvP7666xbtw6Hw8Hw4cMZMWKE374Xwq1z584YjQaK6ptf4R96HeodLUJnKRchPDR69GhuuOEGHn30USoqKg6XYk4++WSysrIwGo1cfPHFLFu2rMW2pk6denhlpWO1+9lnn/HZZ58xbNgwhg8fTl5eHtu2bWPp0qVMmTKFqKgo4uLimDx5cgtnEr5gMplISkykwta83Fphc6fC5OTkYIQVMJLwRcjauXMnRqOx2dgIcC9g8+yzz1JfX8/o0aPJy3PPcnr0PZdDr498/+huktHR0SdsV2vNrbfeyrp161i3bh3bt2/nqquu8unXKbwTFRWFzdn8/7rBqbBYIg7/8Q5VkvD9yOFwBDuEsFVSUsLMmTOZPXv2LxL5jh07yM7O5uabbyY3N/dwwl+1ahW7du3C5XLxxhtvMGbMGMC99vDmzZtxuVy88847xz3nsdo955xzeP7556mpca9jXFhYSHFxMWPHjuXdd9+lvr6e6upqPvjgAz99J8TRtMvF0f0pwqV7hdTw/ai+vp7Y2NhghxE26uvrGTp0KHa7HZPJxKWXXsoNN/xybOAjjzzC119/jcFgYODAgUyYMIHvvvuO3NxcZs+ezfbt2xk3bhxTpkwB4P7772fSpEmkpKSQk5NzOHl70q7FYmHz5s2ceuqpgPsG8CuvvMLw4cOZPn06Q4YMITU1NWRHdrZHDbYGzMbmHQ8jjJrGRjsulyukFi0/mtK6/XSlz8nJ0WvWrAl2GG12xhlnAPDiiy+SmZkZ1FhC1ebNm+nfv3+ww/C7cPk6A0Vrzdlnn8WvulSzq6mnzm3Dq/i8wMrLW6N55513SExMDHKU3lNKfa+1brHfcej+KWsH9u/fH+wQhBBHqKysxG53kBjRfM3DRIv79dE9ukKNJHwfO/IHZvPmzUGMRAhxtMLCQgDSopzN3k+NdL/et29fwGMKJEn4Pvb9998D7qHJq1atCm4wQohmCgoKAEiLbJ7wD73eu3dvwGMKJEn4PvbxokVgMKIjYsjLyyM/Pz/YIQkhmuzZswejgtTI5iUdixGSI93bQ5kkfB/avn07G9avx2WKRJsjQRlO2I1PCBFYe/fuJTVKYzpG5usS2cie/N0BjymQJOH70EsvvYwyReCKiEIbjDQm9+bDDz+itLQ02KEJIYC9e/LpHGk/5rbOUU4KCgpoTz0Xfc2vCV8p9bxSqlgptdGf52kP9uzZw5KlS2hI6QfK/W1t7DIYh9PBwoULgxydCIRPPvmEvn370rt3b+6///5fbLfZbEyfPp3evXszcuRIdu/eHfggw5jWmgMHDpBidR5ze4rVRV19w3HHWYQCfw+8ehGYD7zk5/ME3RtvvAHKgD1tIMYad08dbY3DntiT995/n0svvbTZrIvCf2bf8FeKS8t91l5qpyTmP/zQCfdxOp3MmjWLzz//nIyMDHJzc5k8eXKz2Refe+45EhMT2b59O6+//jo333yz++dGBERdXR31DbbDXTCPltT0fklJScgOmPRrwtdaL1FKZfrzHO2By+Xi68WLsSf2dNfuj9CY2p+GvJ2sWrWK8ePHBynC8FJcWs6OtNN912DRNy3usmrVKnr37k1WVhYAF110Ee+9916zhP/ee+/x97//HYALL7yQ2bNno7WWdRMCpKqqCoAY87FLNjFmV7P9QpHU8H1g165d1NXW4ojr+ottrpgUlNHM+vXrgxCZCJTCwkK6det2+HVGRsbhPt/H2sdkMhEfH09ZWVlA4wxn9fX1AESajp3wrU2Xv3V1dYEKKeCCPpeOUupq4GrouHNRV1RUAKAj3DMnuqKOWCJNGdARUVRWVgYjNCFEk0M3Yw99nuoe03xyQxUGK7YGPeFrrZ8Gngb3XDpBDqdVDs29oezuKwNb91N+3qg1yl7fIefnEJ5LT09vNminoKCA9PT0Y+6TkZGBw+GgsrIy5Odfb0/MZjMA9qYS/iV9ml/J213uPwWhvFSllHR8ID09ncioKEyVBb/YZqwpQjsa6du3bxAiE4GSm5vLtm3b2LVrF42Njbz++uu/WNRk8uTJ/Oc//wHcSyWOHz9e6vcBdKjTRJ3j2N/zQ++HcucKf3fLXAB8B/RVShUopUJy5QeLxcKvzjyTiIp8cNiabTOXbMVitTJ27NggRScCwWQyMX/+fM455xz69+/PtGnTGDhwIHfeeSfvv/8+AFdddRVlZWX07t2bhx9++JhdN4X/xMfHYzQaOGg7dto79H6nTp0CGVZA+buXzsX+bL89mTJlCh988AERRZtoTB8OgLJVYy7fycQp5xMZGdlCC8JXUjsledSzxqv2PHDeeedx3nnnNXvvnnvuOfzcarXKmIwgMhqNpHTqREnTzdujldQbMRmNIV1mC91iVYBlZWUxduxYli5fQWPaIDBFELF/Paam9VFF4LTUZ16Er8yeWRRsPHDMbQW1Rrp3ywjpZQ6lhu9Dv/vd79CORsylW1H2eixl2zn33HNISUkJdmhCCKB3797srzXQeIzBtntqI8jqfVLggwogSfg+1LdvX7IHD8Zakoe5dBva5WTq1KnBDksI0aRfv344NeTXNC9ulDcYONhAyK8uJgnfx84+6yxoqCKiaBO9TzqJHj16BDskIUSTQyOft1U2T/iHXh85MjoUScL3sVNPPRWD0Yiy13O69MwRol1JSkoivWsXtlSYm72/tdKExRLBSSeFdklHbtr6WKdOnXjh+eeprKwM+Y+HQnREQ4YOY8kX+3FpMDR1yd9aaWHgwIEhPegK5ArfL3r06MHgwYMPj+wToe/KK68kNTWVQYMGHXO71pprr72W3r17M3jwYNauXRvgCMUh2dnZ1DTCvlp3b5xau2JPtYEhQ4YGOTL/C+0/ZyIs3XbjbCpLi3zWXnynNO6bO/+E+1x++eXMnj2byy677JjbFy1axLZt29i2bRsrV67kj3/8IytXrvRZjMJzgwcPBtxlnIwYJ9urTGjcfwhCnSR8EXIqS4u4uVeez9p7YEfL+4wdO/aEC5q89957XHbZZSilOOWUU6ioqGD//v106dLFZ3EKz3Tt2pXEhHi2VTYwPt3GtkoTBqXo169fsEPzOynpCBEAnkyfLAJDKUX/AQPZWW0BYGeVmZ49M4mKigpqXIEgCV8IEXb69evHgVpFgwN210TQt194dLCQhC9EAHgyfbIInF69eqGB9eUR1DRqevfuHeyQAkISvhABMHnyZF566SW01qxYsYL4+Hip3wdRz549AVhdHNHsdaiTm7ZC+MDFF1/M4sWLKS0tJSMjg7vvvhu73Q7AzJkzOe+88/j444/p3bs3UVFRvPDCC0GOOLylpaVhNpv4odS95lJHXW3PW5LwRciJ75TmUc8ab9pryYIFC064XSnF448/7quQRBsZjUbSUlMpKNxHhNlMUpJnU2B3dJLwRchpqc+8EAA9s3pRULiPzMweYbPymCR8IURYuv3229m3bx+pqanBDiVgJOELIcKSxWIJm5u1h0gvHdFhaa2DHYJfhfrXJwJPEr7okKxWK2VlZSGbFLXWlJWVYbVagx2KCCFS0hEdUkZGBgUFBZSUlAQ7FL+xWq1kZGQEOwwRQiThiw7JbDaHXf1ViLaSko4QQoQJSfhCCBEmJOELIUSYUO2pl4NSqgTID3YcIaQTUBrsIIQ4Dvn59J0eWuuUlnZqVwlf+JZSao3WOifYcQhxLPLzGXhS0hFCiDAhCV8IIcKEJPzQ9nSwAxDiBOTnM8Ckhi+EEGFCrvCFECJMSMIPAUqpc5VSW5RS25VStxxju0Up9UbT9pVKqczARynCkVLqeaVUsVJq43G2K6XUo00/mzfXXCEAAAPtSURBVOuVUsMDHWM4kYTfwSmljMDjwARgAHCxUmrAUbtdBRzUWvcG5gEPBDZKEcZeBM49wfYJwElNj6uBJwIQU9iShN/xnQxs11rv1Fo3Aq8Dvzlqn98A/2l6/iZwpgqXNd1EUGmtlwDlJ9jlN8BL2m0FkKCU6hKY6MKPJPyOLx3Ye8Trgqb3jrmP1toBVALJAYlOiBPz5OdX+IgkfCGECBOS8Du+QqDbEa8zmt475j5KKRMQD5QFJDohTsyTn1/hI5LwO77VwElKqZ5KqQjgIuD9o/Z5H/ifpucXAl9pGYAh2of3gcuaeuucAlRqrfcHO6hQJStedXBaa4dSajbwKWAEntdab1JK3QOs0Vq/DzwHvKyU2o77BtpFwYtYhBOl1ALgDKCTUqoAuAswA2itnwQ+Bs4DtgN1wBXBiTQ8yEhbIYQIE1LSEUKIMCEJXwghwoQkfCGECBOS8IUQIkxIwhdCiDAhCV+EDKWUUym1Tim1SSn1o1LqRqWUoWlbjlLq0RMce4ZS6sPARfuL8/9dKfWXYJ1fhAfphy9CSb3WeiiAUioVeA2IA+7SWq8B1vjrxEopU9M8RUK0W3KFL0KS1roY93S7s5tGcR6+gldKnd70SWCdUuoHpVRs02FxSqmPmtYWePKITwc1h9pVSl2olHqx6fmLTfutBB48XrtKqb8qpVY3zfd+9xFt/U0ptVUptQzo+//t3bFrU1EUx/Hvz8lABxdddNIO7dYpoKjg0k1QFxcncRPEwcH/QF06dXBxchBxKKigToIYBKVVEVfBUd3EioLx53BvoaRJLKY45P4+8Jb7LudxAzl5OS8593+8LtG23OHH1LL9oe4XsG/g1BXgou2epBngRx3vUvYU+Ag8Bs5Q2kmPcwA4Yrsv6cFgXEmLlF7vXUDAfUnHgXXKP54XKO/DNWB1shVHjJc7/GhRD1iSdAnYs6kU87LuK9AH7gBHtxHrXp0/Ku5iPV5Tkvoc5QPgGLBi+7vtr2ztfxSx45LwY2pJOgj0gc+bx21fBy4AHaAnaW7j1EAIDxnfPTBn/S9xBVyzvVCPWdu3JlhWxD9Lwo+pJGkvcBNYHuwMKumQ7Xe2b1C6jW4k/G7tOroLOAs8r+OfJM3X8dNjrjks7hPgfC3xIGl/faD8DDglqVNr/Sd3au0Ro6SGH9OkI+kNpRvjL+A2sDRk3mVJJ4DfwHvgEXCYkqSXgVngKbBS518FHgJfKL/0mRlx/S1xbf+UNA+8qLtKfgPO2V6TdBd4S/kG8mqShUdsR7plRkQ0IiWdiIhGJOFHRDQiCT8iohFJ+BERjUjCj4hoRBJ+REQjkvAjIhqRhB8R0Yg/WWSprnreSWEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Disbursed', y='Loan_Tenure_Submitted', data=train, hue=\"Disbursed\")\n",
    "plt.xlabel('Disbursed')\n",
    "plt.ylabel('Loan_Tenure_Submitted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Processing_Fee')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Disbursed', y='Processing_Fee', data=train, hue=\"Disbursed\")\n",
    "plt.xlabel('Disbursed')\n",
    "plt.ylabel('Processing_Fee')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0924d089e8>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Disbursed\", hue=\"Filled_Form\",data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0924d02198>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Disbursed\", hue=\"Device_Type\",data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0922e40080>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Disbursed\", hue=\"Var2\",data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0922d4f780>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Disbursed\", hue=\"Source\",data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0922c7a400>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Disbursed\", hue=\"Var4\",data=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "特征处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "删除没有输出值的那一行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([62689]),)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(np.isnan(train.Disbursed))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=train.drop([62689])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([], dtype=int64),)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(np.isnan(train.Disbursed))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "城市特征量统计值小于1000个的，都作为其他城市"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Delhi                   True\n",
       "Bengaluru               True\n",
       "Mumbai                  True\n",
       "Hyderabad               True\n",
       "Chennai                 True\n",
       "Pune                    True\n",
       "Kolkata                 True\n",
       "Ahmedabad               True\n",
       "Jaipur                  True\n",
       "Gurgaon                 True\n",
       "Coimbatore              True\n",
       "Thane                  False\n",
       "Chandigarh             False\n",
       "Surat                  False\n",
       "Visakhapatnam          False\n",
       "Indore                 False\n",
       "Vadodara               False\n",
       "Nagpur                 False\n",
       "Lucknow                False\n",
       "Ghaziabad              False\n",
       "Bhopal                 False\n",
       "Kochi                  False\n",
       "Patna                  False\n",
       "Faridabad              False\n",
       "Madurai                False\n",
       "Noida                  False\n",
       "Gautam Buddha Nagar    False\n",
       "Dehradun               False\n",
       "Raipur                 False\n",
       "Bhubaneswar            False\n",
       "                       ...  \n",
       "Jashpur                False\n",
       "Fazilka                False\n",
       "Chandel                False\n",
       "DHORAJI                False\n",
       "Sheopur                False\n",
       "Gopal Ganj             False\n",
       "Umaria                 False\n",
       "Rampur                 False\n",
       "RADHANPUR              False\n",
       "Magadh                 False\n",
       "Dungarpur              False\n",
       "Khagaria               False\n",
       "Munger                 False\n",
       "Beawar                 False\n",
       "Narayanpur             False\n",
       "Lakshadweep            False\n",
       "Seoni                  False\n",
       "Dantewada              False\n",
       "Gadwal                 False\n",
       "Poonch                 False\n",
       "Shahpura               False\n",
       "Muktsar                False\n",
       "Lalitpur               False\n",
       "Nalbari                False\n",
       "SAYAN                  False\n",
       "Tamenglong             False\n",
       "Himatnagar             False\n",
       "Narsinghpur            False\n",
       "KHAMBHAT               False\n",
       "Champawat              False\n",
       "Name: City, Length: 697, dtype: bool"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['City'].value_counts()>1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "City_name=['Delhi',                  \n",
    "'Bengaluru',               \n",
    "'Mumbai',                  \n",
    "'Hyderabad',               \n",
    "'Chennai',                 \n",
    "'Pune',                    \n",
    "'Kolkata',                \n",
    "'Ahmedabad',             \n",
    "'Jaipur',                 \n",
    "'Gurgaon',                 \n",
    "'Coimbatore']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def fun1(x):\n",
    "    if x in City_name:\n",
    "        return x\n",
    "    else:\n",
    "        return 'othercity'\n",
    " \n",
    "train['City']= train['City'].apply(lambda x: fun1(x))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# Existing_EMI如果有为1，没有为0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fun2(x):\n",
    "    if x>0:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    " \n",
    "train['Existing_EMI']= train['Existing_EMI'].apply(lambda x: fun2(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# employname 有为1，没有为o\n",
    "train['Employer_Name'] = train['Employer_Name'].apply(lambda x: 0 if x=='0' else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Salary_Account，数目小于1000，设置为others\\\n",
    "Salary_Account_name=['HDFC Bank',                                          \n",
    "'ICICI Bank',                                         \n",
    "'State Bank of India',                                \n",
    "'Axis Bank',                                           \n",
    "'Citibank',                                            \n",
    "'Kotak Bank',                                          \n",
    "'IDBI Bank',                                           \n",
    "'Punjab National Bank',                                \n",
    "'Bank of India',                                       \n",
    "'Bank of Baroda']\n",
    "def fun3(x):\n",
    "    if x in Salary_Account_name:\n",
    "        return x\n",
    "    else:\n",
    "        return 'otheraccout'\n",
    "train['Salary_Account']= train['Salary_Account'].apply(lambda x: fun3(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30    7134\n",
       "31    6754\n",
       "29    6628\n",
       "32    6451\n",
       "28    6223\n",
       "33    5951\n",
       "34    5114\n",
       "35    4545\n",
       "27    4517\n",
       "36    3942\n",
       "26    3410\n",
       "0     3351\n",
       "38    3350\n",
       "37    3141\n",
       "39    2137\n",
       "25    1946\n",
       "40    1811\n",
       "41    1441\n",
       "42    1253\n",
       "43    1155\n",
       "24     965\n",
       "48     946\n",
       "44     907\n",
       "45     770\n",
       "46     710\n",
       "47     571\n",
       "49     565\n",
       "50     500\n",
       "23     480\n",
       "22     211\n",
       "21     123\n",
       "3       17\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def fun4(x):\n",
    "    if x < 0:\n",
    "        return 0\n",
    "    else:\n",
    "        return x\n",
    "\n",
    "train['Age'] = train['Age'].apply(lambda x: fun4(x))\n",
    "train['Age'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 87019 entries, 0 to 87019\n",
      "Data columns (total 29 columns):\n",
      "ID                       87019 non-null object\n",
      "Gender                   87019 non-null object\n",
      "City                     87019 non-null object\n",
      "Monthly_Income           87019 non-null int64\n",
      "DOB                      87019 non-null object\n",
      "Lead_Creation_Date       87019 non-null datetime64[ns]\n",
      "Loan_Amount_Applied      86948 non-null float64\n",
      "Loan_Tenure_Applied      86948 non-null float64\n",
      "Existing_EMI             87019 non-null int64\n",
      "Employer_Name            87019 non-null int64\n",
      "Salary_Account           87019 non-null object\n",
      "Mobile_Verified          87019 non-null object\n",
      "Var5                     87019 non-null object\n",
      "Var1                     87019 non-null object\n",
      "Loan_Amount_Submitted    52407 non-null float64\n",
      "Loan_Tenure_Submitted    52407 non-null float64\n",
      "Interest_Rate            27726 non-null float64\n",
      "Processing_Fee           27420 non-null float64\n",
      "EMI_Loan_Submitted       27726 non-null object\n",
      "Filled_Form              87019 non-null object\n",
      "Device_Type              87019 non-null object\n",
      "Var2                     87019 non-null object\n",
      "Source                   87019 non-null object\n",
      "Var4                     87019 non-null int64\n",
      "LoggedIn                 87019 non-null int64\n",
      "Disbursed                87019 non-null float64\n",
      "Age                      87019 non-null int64\n",
      "Lead_Creation_month      87019 non-null int64\n",
      "Employer_Name_Missing    87019 non-null int64\n",
      "dtypes: datetime64[ns](1), float64(7), int64(8), object(13)\n",
      "memory usage: 19.9+ MB\n"
     ]
    }
   ],
   "source": [
    " train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 样本数量太多了，运行比较慢，为了作业，先取一部分数据分析，又由于负样本数据实在太多了，正样本太少，所以从负样本中随机采样8000个\n",
    "#而后再新的正负样本中在设置类别权重\n",
    "train=train[train['Disbursed']==1].append(train[train['Disbursed']==0].sample(n=15000,random_state=10, axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    15000\n",
       "1.0     1273\n",
       "Name: Disbursed, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Disbursed'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "train=train.drop(['DOB','Lead_Creation_Date','Employer_Name_Missing'], axis = 1)\n",
    "\n",
    "train.to_csv('FE_homework_WK3.csv',index = False,header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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